Computing Diversity

Decision Team
Diversity of membership in decision-making teams leads to better outcomes. By Rawpixel.com/Shutterstock

17 July 2019 – It’s come to my attention that a whole lot of people don’t know how to calculate a diversity score, or even that such a thing exists! How can there be so much discussion of diversity and so little understanding of what the word means? In this post I hope to give you a peek behind the curtain, and maybe shed some light on what diversity actually is and how it is measured.

This topic is of particular interest to me at present because momentum is building to make a study of diversity in business-decision making the subject of my doctoral dissertation in Business Administration. Specifically, I’m looking at how decision-making teams (such as boards of directors) can benefit from membership diversity, and what can go wrong.

Estimating Diversity

The dictionary definition of diversity is: “the condition of having or being composed of differing elements.”

So, before we can quantify the diversity of any group, we’ve got to identify what makes different elements different. This, by the way, is all basic set theory. In different contexts what we mean by “different” may vary. When we’re talking about group decision making in a business context, it gets pretty complicated.

A group may be subdivided, or “stratified” along various dimensions. For example, a team of ten people sitting around a table trying to figure out what to do next about, say, a new product could be subdivided in various ways. One way to stratify such a group is by age. You’d have so many individuals in their 20’s, so many might be in their 30’s, and so forth up to the oldest group being aged 50 or more. Another (perhaps more useful) way to subdivide them is by specialty. There may be so many software engineers, so many hardware engineers, so many marketers, and so forth. These days stratifying teams by gender, ethnicity, educational level or political persuasion could be important. What counts as diversity depends on what the team is trying to decide.

The moral of this story is that a team might score high in diversity along one dimension and very poorly along another. I’m not going to say any more about diversity’s multidimensional nature in this essay, however. We have other fish to fry today.

For now, let’s assume a one-dimensional diversity index. What we pick for a dimension makes little difference to the mathematics we use. Let’s just imagine a medium-sized group of, say, ten individuals and stratify them according to the color of tee-shirts they happen to be wearing at the moment.

What the color of their tee-shirts could possibly mean for the group’s decisions about new-product development I can’t imagine, and probably wouldn’t care anyway. It does, however, give us a way to stratify the sample. Let’s say their shirt colors fall out as in Table 1. So, we’ve got five categories of team members stratified by tee-shirt color.Table 1: Tee-Shirt Colors

NOTE: The next bit is mathematically rigorous enough to give most people nosebleeds. You can skip over it if you want to, as I’m going to follow it with a more useful quick-and-dirty estimation method.

The Gini–Simpson diversity index, which I consider to be the most appropriate for evaluating diversity of decision-making teams, has a range of zero to one, with zero being “everybody’s the same” and one being “everybody’s different.” We start by asking: “What is the probability that two members picked at random have the same color tee shirt?”

If you’ve taken my statistical analysis course, you’ll likely loathe remembering that the probability of picking two things from a stratified data set, and having them both fall into the same category is:

Equation 1

Where λ is the probability we want, N is the number of categories (in this case 5), and P is the probability that, given the first pick falling into a certain category (i) the second pick will be in the same category. The superscript 2 just indicates that we’re taking members two at a time. Basically P is the number of members in category i divided by the total number of members in all categories. Thus, if the first pick has a blue tee-shirt, then P is 3/10 = 0.3.

This probability is high when diversity is low, and low when diversity is high. The Gini-Simpson index makes more intuitive sense by simply subtracting that probability from unity (1.0) to get something that is low when diversity is low, and high when diversity is high.

NOTE: Here’s where we stop with the fancy math.

Guesstimating Diversity

Veteran business managers (at least those not suffering from pathological levels of OCD) realize that the vast majority of business decisions – in fact most decisions in general – are not made after extensive detailed mathematical analysis like what I presented in the previous section. In fact, humans have an amazing capacity for making rapid decisions based on what’s called “fuzzy logic.”

Fuzzy logic recognizes that in many situations, precise details may be difficult or impossible to obtain, and may not make a significant difference to the decision outcome, anyway. For example, deciding whether to step out to cross a street could be based on calculations using precise measurements of an oncoming car’s speed, distance, braking capacity, and the probability that the driver will detect your presence in time to apply the brakes to avoid hitting you.

But, it’s usually not.

If we had to make the decision by the detailed mathematical analysis of physical measurements, we’d hardly ever get across the street. We can’t judge speed or distance accurately enough, and have no idea whether the driver is paying attention. We don’t, in general, make these measurements, then apply detailed calculations using Gallilean Transformations to decide if now is a safe time to cross.

No, we have, with experience over time, developed a “gut feel” for whether it’s safe. We use fuzzy categories of “far” and “near,” and “slow” or “fast.” Even the terms “safe” and “unsafe” have imprecise meanings, gradually shifting from one to the other as conditions change. For example “safe to cross” means something quite different on a dry, sunny day in summertime, than when the pavement has a slippery sheen of ice.

Group decision making has a similar fuzzy component. We know that the decision team we’ve got is the decision team we’re going to use. It makes no difference whether it’s diversity score is 4.9 or 5.2, what we’ve got is what we’re going to use. Maybe we could make a half-percent improvement in the odds of making the optimal decision by spending six months recruiting and training a blind Hispanic woman with an MBA to join the team, but are we going to do it? Nope!

We’ll take our chances with the possibly sub-optimal decision made by the team we already have in place.

Hopefully we’re not trying to work out laws affecting 175 million American women with a team consisting of 500 old white guys, but, historically, that’s the team we’ve had. No wonder we’ve got so many sub-optimal laws!

Anyway, we don’t usually need to do the detailed Gini-Simpson Diversity Index calculation to guesstimate how diverse our decision committee is. Let’s look at some examples whose diversity indexes are easy to calculate. That will help us develop a “gut feel” for diversity that’ll be useful in most situations.

So, let’s assume we look around our conference room and see six identical white guys and six identical white women. It’s pretty easy to work out that the team’s diversity index is 0.5. The only way to stratify that group is by gender, and the two strata are the same size. If our first pick happens to be a woman, then there’s a 50:50 chance that the second pick will be a woman, too. One minus that probability (0.5) equals 0.5.

Now, let’s assume we still have twelve team members, but eleven of them are men and there’s only one token woman. If your first pick is the woman, the probability of picking a woman again is 1/12 = 0.8. (The Gini-Simpson formula lets you pick the same member twice.) If, on the other hand, your first pick is a man, the probability that the second pick will also be a man is 11/12 = 0.92. I plugged all this into an online Gini-Simpson-Index calculator (‘cause I’m lazy) and it returned a value of 26%. That’s a whole lot worse.

Let’s see what happens when we maximize diversity by making everyone different. That means we end up stratifying the members into twelve segments. After picking one member, the odds of the second pick being identical are 1/12 = 0.8 for every segment. The online calculator now gives us a diversity index of 91.7%. That’s a whole lot better!

What Could Possibly Go Wrong?

There are two main ways to screw up group diversity: group-think and group-toxicity. These are actually closely related group-dynamic phenomena. Both lower the effective diversity.

Group-think occurs when members are too accommodating. That is, when members strive too hard to reach consensus. They look around to see what other members want to do, and agree to it without trying to come up with their own alternatives. This produces sub-optimal decisions because the group fails to consider all possible alternatives.

Toxic group dynamics occurs when one or more members dominate the conversation either by being more vocal or more numerous. Members with more reticent personalities fail to speak up, thus denying the group their input. Whenever a member fails to speak up, they lower the group’s effective diversity.

A third phenomenon that messes up decision making for  high-diversity teams is that when individual members are too insistent that their ideas are the best, groups often fail to reach consensus at all. At that point more diversity makes reaching consensus harder. That’s the problem facing both houses of the U.S. Congress at the time of this writing.

These phenomena are present to some extent in every group discussion. It’s up to group leadership to suppress them. In the end, creating an effective decision-making team requires two elements: diversity in team membership, and effective team leadership. Membership diversity provides the raw material for effective team decision making. Effective leadership mediates group dynamics to make it possible to maximize the team’s effective diversity.

The ASEAN Community

ASEAN Summer 2019 Logo
The 34th Asean Summit Bangkok: Advancing Partnership For Sustainability, 23 June 2019

Apologies to all the folks whose words I’ve expropriated for this piece with insufficient attribution – mostly from Wikipedia and ASEAN sources. It’s already taken three days to piece this essay together and I’m trying to get it published while the dateline is still good! Just ONE more editing pass.

26 June 2019 – This is an appropriate time to visit a little-known and -acknowledged regional international community being developed in Southeast Asia: ASEAN. Last Sunday (23 June 2019) marked the 34th meeting of the ASEAN Summit in Bangkok, Thailand

ASEAN was established on 8 August 1967 with the signing of the ASEAN Declaration (Bangkok Declaration) by the five founding member states, namely Indonesia, Malaysia, Philippines, Singapore and Thailand. Five additional member states – Brunei Darussalam (1984), Viet Nam (1995), Lao PDR and Myanmar (1997), and Cambodia (1999) – joined later to complete the ten member states of ASEAN today. An eleventh nation, Timor-Leste (in English: East Timor) has applied for membership.

The creation of ASEAN was originally motivated by a common fear of communism among the original five founding member states. ASEAN achieved greater cohesion in the mid-1970s following a change in the international balance of power after the end of the Vietnam War in 1975. The region’s dynamic economic growth during the 1970s strengthened the organization, enabling ASEAN to adopt a unified response to Vietnam’s invasion of Cambodia in 1979.

ASEAN’s first summit meeting, held in Bali, Indonesia in 1976, resulted in an agreement on several industrial projects and the signing of a Treaty of Amity and Cooperation, and a Declaration of Concord.

The end of the Cold War between the West and the Soviet Union at the end of the 1980s allowed ASEAN countries to exercise greater political independence in the region, and in the 1990s ASEAN emerged as a leading voice on regional trade and security issues.

ASEAN has a total population of 642 million people, which is nearly double that of the United States (327 million), and twenty-five percent larger than that of the European Union (513 million). Its average annual income per person, however, is only $4,308.00, putting it between the Israeli-occupied West Bank and Mauritania in the Western Sahara as far as average wealth per person is concerned. That means its people still have a long way to go! Its GDP growth rate, however, is 5.3% per annum, which is comparable to that of Egypt or Pakistan and ahead of the average for even emerging and developing countries.

Why Do We Care?

Why should Americans care about ASEAN?

First, it has aspirations to be a regional intergovernmental organization similar to the European Union in an region where the United States has economic and political interests. Their charter specifically calls for adherence to basic principles in line with those of the United States and other Western democracies. Notably the ASEAN charter specifically calls for adherence to democratic principles and maintaining the region as a nuclear-free zone.

Second, as a large and (aspirationally) politically and economically cohesive regional intergovernmental organization, ASEAN can provide a large and (again aspirationally) economically powerful ally in Southeast Asia to counterbalance Chinese efforts to extend its hegemony in the region. Especially, their actions reveal a desire to cooperate with the United States and its allies. For example, the charter refers in numerous places to working with United Nations principles and protocols, and establishes English as the ASEAN working language.

Organization

The ASEAN Community is comprised of three “pillars:” the ASEAN Political-Security Community, the ASEAN Economic Community and the ASEAN Socio-Cultural Community. Each pillar has its own Blueprint, and, together with the Initiative for ASEAN Integration (IAI) Strategic Framework and IAI Work Plan Phase II (2009-2015), they form the Roadmap for an ASEAN Community.

The figure below shows ASEAN’s top organization levels. At the top is the ASEAN Summit, comprised of the heads of state or government of the member states. By charter, they meet together twice a year, hosted by the member state holding the ASEAN Chairmanship, which cycles through the member states. At present, that is Thailand (Prime Minister General Prayut Chan-o-cha), so the latest meeting was held on 23 June 2019 in the Thai capital, Bangkok.

ASEAN Org Chart
ASEAN Organizational Structure.

At the next level, ASEAN is divided into three Community Councils that represent the three pillars of ASEAN activity:

  1. The ASEAN Political-Security Community Council

  2. The ASEAN Economic Community Council

  3. The ASEAN Socio-Cultural Community Council

Each of the three Community Councils has their own makeup and sphere of activity. The ASEAN Coordinating Council, for example, comprises the Foreign Ministers of the ASEAN member states and meets at least twice a year, not only to prepare the meetings of the ASEAN Summit, but to undertake other tasks provided for in the Charter, or for such other functions as may be assigned by the ASEAN Summit. For example, the Coordinating Council coordinates implementation of agreements and decisions of the ASEAN Summit.

In order to realize the objectives of each of the three pillars of the ASEAN Community, each ASEAN Community Council ensures the implementation of the relevant decisions of the ASEAN Summit; coordinates the work of the different sectors under its purview; ensures implementation of Summit decisions on issues that cut across the other Community Councils; and submits reports and recommendations to the ASEAN Summit on matters under its purview.

Each member state designates its own national representatives for each ASEAN Community Council. In addition, each ASEAN member state establishes an ASEAN National Secretariat that serves as a national focal point, the repository of information on all ASEAN matters at the national level, coordinates the implementation of ASEAN decisions at the national level, coordinates and supports the national preparations of ASEAN meetings, promotes ASEAN identity and awareness at the national level, and contributes to ASEAN community building.

Political-Security Community

ASEAN member states pledge to rely exclusively on peaceful processes in the settlement of intra-regional differences and with regard to their security. They are fundamentally linked to one another and bound by geographic location, as well as by a common vision and objectives.

The ASEAN Political-Security Community (APSC) aims to ensure that countries in the region live at peace with one another and with the world in a just, democratic and harmonious environment. The APSC Blueprint envisages ASEAN to be a rules-based community of shared values and norms; a cohesive, peaceful, stable and resilient region with shared responsibility for comprehensive security; and a dynamic and outward-looking region in an increasingly integrated and interdependent world. The APSC’s normative activities include: political development; shaping and sharing of norms; conflict prevention; conflict resolution; post-conflict peace building; and implementing mechanisms.

Economic Community

The ASEAN Economic Community (AEC) has a Consolidated Strategic Action Plan (CSAP) that includes strategic measures in the AEC Blueprint 2025 that takes into account the relevant sector work plans, and is reviewed periodically to account for developments in each sector.

The inaugural issue of the ASEAN Economic Integration Brief (AEIB) was released on 30 June 2017. The AEIB provides regular updates on ASEAN economic integration progress and outcomes, and is a demonstration of ASEAN’s commitment to strengthen communication and outreach to raise stakeholder awareness of the AEC.

The ASEAN Good Regulatory Practice (GRP) Core Principles was adopted at the 50th AEM Meeting in 29 August 2018 and subsequently endorsed by the AEC Council. It provides a practical, non-binding set of principles to assist ASEAN member states to improve their regulatory practice and foster ASEAN-wide regulatory cooperation.

Socio-Cultural Community

At the heart of the ASEAN Socio-Cultural Community (ASCC) is the commitment to lift the quality of life of ASEAN peoples through cooperative activities that are people-oriented, people-centered, environmentally friendly, and geared toward the promotion of sustainable development through member states’ cooperation on a wide range of areas including: culture and information, education, youth and sports, health, social welfare and development, women and gender, rights of the women and children, labor, civil service, rural development and poverty eradication, environment, transboundary haze-pollution, disaster management and humanitarian assistance.

Free-Trade Zone

The AEC aims to “implement economic integration initiatives” to create a single market across ASEAN member states. Its blueprint, adopted during the 13th ASEAN Summit (2007) in Singapore, serves as a master plan guiding the establishment of the community. Its characteristics include a single market and production base, a highly competitive economic region, a region of fair economic development, and a region fully integrated into the global economy.

The areas of co-operation include human resources development; recognition of professional qualifications; closer consultation on macroeconomic and financial policies; trade financing measures; enhanced infrastructure and communications connectivity; development of electronic transactions through e-ASEAN; integrating industries across the region to promote regional sourcing; and enhancing private sector involvement.

The AEC is the embodiment of the ASEAN’s vision of “a stable, prosperous and highly competitive ASEAN economic region in which there is a free flow of goods, services, investment and a freer flow of capital, equitable economic development and reduced poverty and socio-economic disparities.”

The average economic growth of member states from 1989 to 2009 was between 3.8% and 7%. This was greater than the average growth of APEC, which was 2.8%. The ASEAN Free Trade Area (AFTA), established on 28 January 1992, includes a Common Effective Preferential Tariff (CEPT) to promote the free flow of goods between member states.

ASEAN member states have made significant progress in the lowering of intra-regional tariffs through the CEPT. More than 99 percent of the products in Brunei Darussalam, Indonesia, Malaysia, the Philippines, Singapore and Thailand, have been brought down to the 0-5 percent tariff range. ASEAN’s newer members, namely Cambodia, Laos, Myanmar and Viet Nam, are not far behind.

ASEAN member states have also resolved to work on the elimination of non-tariff barriers, which includes, among others, the process of verification and cross-notification; updating the working definition of Non-Tariff Measures (NTMs)/Non-Tariff Barriers (NTBs); the setting-up of a database on all NTMs maintained by member states; and the eventual elimination of unnecessary and unjustifiable non-tariff measures.

I led this essay off with the comment that ASEAN does not seem to get the attention it deserves, at least in U.S. national media. Certainly, U.S. President Donald Trump seems to feel it’s not worth a tweet. The closest I was able to find with a quick Internet search was a report that he insulted Philippines President Rodrigo Duterte before meeting him on the sidelines of the Winter 2017 ASEAN Summit meeting!

That said, I must report that I became interested in ASEAN through a segment in Fareed Zacharia’s GPS show on CNN. So, not everybody is completely ignoring what I’ve come to realize is potentially an important regional intergovernmental organization.

I encourage you to learn more about ASEAN by visiting the various links peppering this column. Maybe together we can generate more interest in what could be a powerful U.S. ally in the Eastern Pacific.

The Fluidity of Money

Money exchange
Money is created in the exchange of credit for debt. Image by bluedog studio/shutterstock

19 June 2016 – I’m supposed to have some passing understanding of economics and accounting. I have, after all, a Master’s degree in Business Administration, for which I had to study Macroeconomics and Microeconomics, as well as Cost and Financial Accounting.

Howsomever, while trying to make sense of what folks call “Modern Monetary Theory” it dawned on me that, not only didn’t I have a clear concept of what money actually is, but the people babbling on about money and monetary policy aren’t any clearer on the concept than I am. A review of the differences between neoclassical economics based on Keynsian ideas and so-called Modern Monetary Theory reveals an incomplete understanding of money.

We all think we know what money is, and spout long winded and erudite-sounding loads of gobbledygook that only serve to prove, beyond a shadow of a doubt, that none of us have a clue what the stuff actually is!

I find that situation intolerable, and have set out to change it by trying real hard to come up with a theory that makes sense of all the stupid things we do with and say about money.

Now, I’m not a financial wizard, or a prize-winning economist, or even a whiz-bang developer of computer models of the global economy. I’m just some schmuck with some basic math ability, a little time on my hands, and the desire to make sense of something that it seems the “experts” haven’t wrapped their brains around, yet. So, I’ve thought about this problem a bit, and have a hint of an answer that I want to run up the flagpole to see if anyone salutes.

If this essay triggers something in the brain of somebody smart that sets him, her or it thinking in a new direction about money, I’ll count it time well spent.

So, here goes … .

In science, we try to make sense of anything we don’t fully comprehend by developing some kind of conceptual model that helps us predict what will happen in any given situation. The fact that we currently haven’t a clue what will actually happen when, for example, the Federal Government runs up huge deficits for a very long time, indicates that we’re very far from knowing what we’re talking about with regard to money.

I generally try to model things poorly understood through analogy with things that are well understood. I’ve developed a two-fluid model of money by analogy to certain ideas in classical physics. It seems to work decently for the situations I’ve applied it to.

Analogy with Momentum

Specifically, the model draws an analogy with Newtonian momentum, which is a conserved vector quantity – meaning that the total momentum in a closed system cannot be changed, and that the quantity involves both a magnitude and a spatial direction.

For our analogy to be useful, we need to also use the idea of generalized coordinates, which allow the idea of “direction” to extend beyond strictly cartesian spatial coordinates (motion in straight lines). For example, a bicycle drive chain wraps around two sprockets and has flexible spans linking them, so its motion certainly does not follow along a single cartesian coordinate, yet there is a well-defined path along which any two points on the chain follow each other, maintaining their separation (measured along the path). That allows us to measure motion along the path by a generalized coordinate.

In Newtonian mechanics, momentum is exchanged between objects, which are thought of as components of a system, through the action of forces. Mathematically, the magnitude and direction of the force equals the rate of flow of momentum between the objects.

Newton’s third law, which states that every force is paired with an equal and opposite reaction force, is just an expression of conservation of momentum in that every force (representing a transfer of momentum from one object to another) is paired with an equal and opposite transfer of momentum from the second object to the first. This takes care of maintaining conservation of momentum.

Take, for example, a person stepping off a boat onto a dock. At first, everything is (as seen from the perspective of the dock) stationary. The momentum of an object is defined as the object’s mass (amount of material) times its velocity (a vector combining speed and direction). Since both the person and the boat are stationary (meaning they both have a velocity of zero), the total momentum of the system of person + boat is zero.

Then, the person applies a force to the boat in a direction away from the dock. The Newton’s-third-law reaction force is a push by the boat on the person toward the dock. That’s how the person actually gets to the dock. The boat pushes him/her toward it!

The boat moves away from the dock. The person moves toward the dock. So, the directions of the two momenta are opposite. The speeds of the person and boat automatically (or maybe you’d like to say “magically”) adjust to keep the total momentum of the system equal to zero at all times. That is, at every instant the momentum of the person is equal and opposite to the momentum of the boat.

Money

In the theory of money that I’m proposing, money itself is analogous to momentum. Altogether, it’s conserved. That is, it cannot be created or destroyed. There’s always the same amount of “money” – zero!

What we’re used to thinking of as “money” is only half the story, which is why there’s so much confusion over it. Borrowing from double-entry bookkeeping, we’ll call what we usually think of as money as credit. Everyone who understands double-entry bookkeeping knows that for every credit, there is an equal (and opposite) entry called a debit. For our purposes, we’ll shorten that word to something we’re all familiar with: debt.

Debt is the other side of the analogy, which we tend to ignore and that accounts for all the confusion.

We’re going to visualize credit and debt as fluids because they’re measured as continuous, as opposed to quantized, variables. That means that they’re representable by real numbers as opposed to integers. So, nobody has a problem with dividing seven dollars ($7) into two portions each containing three and a half dollars ($3.50). Current usage is to round everything to the nearest cent, or hundredth of a dollar, but that’s for convenience and not wanting to be bothered with truly small change.

At one time, we had half-penny ($0.005) coins, but we don’t do that anymore.

Okay, so “money” actually represents credit and debt in equal amounts, which consequently always add up to zero. Whenever money is created, it’s created as equal amounts of credit and debt.

Money creation always requires activity by two cooperating entities: a creditor and a debtor. Credit is created and transferred from the creditor to the debtor. An equal quantity of debt is created and flows from the debtor to the creditor. “Money” consists of these paired fluids, which flow through the economy via paired interactions between creditors and debtors. Money is created by an interaction that creates equal amounts of credit and debt, and the words “creditor” and “debtor” simply indicate the direction of flow.

Once created, the money flows around in the economy through paired transactions in which credit flows one way and debt the other.

Wealth

This visualization allows us to separate the concepts of “money” and “wealth.” Wealth refers to tangible and intangible assets, such as commodities and intellectual property. Wealth is very definitely not conserved. When a contractor builds a house, he or she creates wealth from, essentially, nothing. The contractor then sells the house to the new owner in a binary transaction that transfers credit to the contractor and debt to the owner.

We’ll leave out discussion of what happens to the wealth represented by the house, since this essay is about money, and money is not wealth.

The owner previously got the credit through a transaction with a lender in which money was created as a transfer of credit to the owner and debt to the lender. The lender can then, for example, package the debt up into something called a “collateralized debt obligation,” and exchange it with somebody else for an equivalent amount of credit. The lender then transfers that credit to another prospective home owner in exchange for an equivalent amount of debt, and the merry-go-round keeps turning.

Unlike wealth, which was created from nothing, the total of credit minus debt in the system remains zero at all times.

It is interesting to note that wealth appears through the creation of a pattern in the physical universe. For example, bricks used by a contractor to build a house start out as a less-organized pile. The contractor creates wealth by arranging those bricks in a house-like pattern. The owner has no use for the disorganized pile of bricks, but has a use for them when arranged as a house. Similarly, the contractor had no use for the raw clay that went into the bricks until the brick manufacturer rearranged it into the pattern we call “bricks.”

Historically, folks’ fascination with the credit side of money has led them to confuse “money” with “wealth.” They’re entirely different things. One is a medium of exchange related to entries in a bookkeeper’s ledger, the other is a real thing related to patterns in the physical world.

I hope this essay manages to help make sense of the money nonsense!

What is This “Robot” Thing, Anyway?

Robot thinking
So, what is it that makes a robot a robot? Phonlamai Photo/Shutterstock

6 March 2019 – While surfing the Internet this morning, in a valiant effort to put off actually getting down to business grading that pile of lab reports that I should have graded a couple of days ago, I ran across this posting I wrote in 2013 for Packaging Digest.

Surprisingly, it still seems relevant today, and on a subject that I haven’t treated in this blog, yet. It being that I’m planning to devote most of next week to preparing my 2018 tax return, I decided to save some writing time by dusting it off and presenting it as this week’s posting to Tech Trends. I hope the folks at Packaging Digest won’t get their noses too far out of joint about my encroaching on their five-year-old copyright without asking permission.

By the way, this piece is way shorter than the usual Tech Trends essay because of the specifications for that Packaging Digest blog, which was entitled “New Metropolis” in homage to Fritz Lang’s 1927 feature film entitled Metropolis, which told the story of a futuristic mechanized culture and an anthropomorphic robot that a mad scientist creates to bring it down. The “New Metropolis” postings were specified to be approximately 500 words long, whereas Tech Trends postings are planned to be 1,000-1,500 words long.

Anyway, I hope you enjoy this little slice of recent history.


11 November 2013 – I thought it might be fun—and maybe even useful—to catalog the classifications of these things we call robots.

Lets start with the word robot. The idea behind the word robot grows from the ancient concept of the golem. A golem was an artificial person created by people.

Frankly, the idea of a golem scared the bejeezus out of the ancients because the golem stands at the interface between living and non-living things. In our enlightened age, it still scares the bejeezus out of people!

If we restricted the field to golems—strictly humanoid robots, or androids—we wouldnt have a lot to talk about, and practically nothing to do. The things havent proved particularly useful. So, I submit that we should expand the robot definition to include all kinds of human-made artificial critters.

This has, of course, already been done by everyone working in the field. The SCARA (selective compliance assembly robot arm) machines from companies like Kuka, and the delta robots from Adept Technologies clearly insist on this expanded definition. Mobile robots, such as the Roomba from iRobot push the boundary in another direction. Weird little things like the robotic insects and worms so popular with academics these days push in a third direction.

Considering the foregoing, the first observation is that the line between robot and non-robot is fuzzy. The old 50s-era dumb thermostats probably shouldnt be considered robots, but a smart, computer-controlled house moving in the direction of the Jarvis character in the Ironman series probably should. Things in between are – in between. Lets bite the bullet and admit were dealing with fuzzy-logic categories, and then move on.

Okay, so what are the main characteristics symptomatic of this fuzzy category robot?

First, its gotta be artificial. A cloned sheep is not a robot. Even designer germs are non-robots.
Second, its gotta be automated. A fly-by-wire fighter jet is not a robot. A drone linked at the hip to a human pilot is not a robot. A driverless car, on the other hand, is a robot. (Either that, or its a traffic accident waiting to happen.)

Third, its gotta interact with the environment. A general-purpose computer sitting there thinking computer-like thoughts is not a robot. A SCARA unit assembling a car is. I submit that an automated bill-paying system arguing through the telephone with my wife over how much to take out of her checkbook this month is a robot.

More problematic is a fourth direction—embedded systems, like automated houses—that beg to be admitted into the robotic fold. I vote for letting them in, along with artificial intelligence (AI) systems, like the robot bill paying systems my wife is so fond of arguing with.

Finally (maybe), its gotta be independent. To be a robot, the thing has to take basic instruction from a human, then go off on its onesies to do the deed. Ideally, you should be able to do something like say, Go wash the car, and itll run off as fast as its little robotic legs can carry it to wash the car. More chronistically, you should be able to program it to vacuum the living room at 4:00 a.m., then be able to wake up at 6:00 a.m. to a freshly vacuumed living room.

Luddites RULE!

LindaBucklin-Shutterstock
Momma said there’d be days like this! (Apologies to songwriters Luther Dixon and Willie Denson, and, of course, the Geico Caveman.) Linda Bucklin/Shutterstock

7 February 2019 – This is not the essay I’d planned to write for this week’s blog. I’d planned a long-winded, abstruse dissertation on the use of principal component analysis to glean information from historical data in chaotic systems. I actually got most of that one drafted on Monday, and planned to finish it up Tuesday.

Then, bright and early on Tuesday morning, before I got anywhere near the incomplete manuscript, I ran headlong into an email issue.

Generally, I start my morning by scanning email to winnow out the few valuable bits buried in the steaming pile of worthless refuse that has accumulated in my Inbox since the last time I visited it. Then, I visit a couple of social media sites in an effort to keep my name if front of the Internet-entertained public. After a couple of hours of this colossal waste of time, I settle in to work on whatever actual work I have to do for the day.

So, finding that my email client software refused to communicate with me threatened to derail my whole day. The fact that I use email for all my business communications, made it especially urgent that I determine what was wrong, and then fix it.

It took the entire morning and on into the early afternoon to realize that there was no way I was going to get to that email account on my computer, find out that nobody in the outside world (not my ISP, not the cable company that went that extra mile to bring Internet signals from that telephone pole out there to the router at the center of my local area network, or anyone else available with more technosavvy than I have) was going to be able to help. I was finally forced to invent a work around involving a legacy computer that I’d neglected to throw in the trash just to get on with my technology-bound life.

At that point the Law of Deadlines forced me to abandon all hope of getting this week’s blog posting out on time, and move on to completing final edits and distribution of that press release for the local art gallery.

That wasn’t the last time modern technology let me down. In discussing a recent Physics Lab SNAFU, Danielle, the laboratory coordinator I work with at the University said: “It’s wonderful when it works, but horrible when it doesn’t.”

Where have I heard that before?

The SNAFU Danielle was lamenting happened last week.

I teach two sections of General Physics Laboratory at Florida Gulf Coast University, one on Wednesdays and one on Fridays. The lab for last week had students dropping a ball, then measuring its acceleration using a computer-controlled ultrasonic detection system as it (the ball, not the computer) bounces on the table.

For the Wednesday class everything worked perfectly. Half a dozen teams each had their own setups, and all got good data, beautiful-looking plots, and automated measurements of position and velocity. The computers then automatically derived accelerations from the velocity data. Only one team had trouble with their computer, but they got good data by switching to an unused setup nearby.

That was Wednesday.

Come Friday the situation was totally different. Out of four teams, only two managed to get data that looked even remotely like it should. Then, one team couldn’t get their computer to spit out accelerations that made any sense at all. Eventually, after class time ran out, the one group who managed to get good results agreed to share their information with the rest of the class.

The high point of the day was managing to distribute that data to everyone via the school’s cloud-based messaging service.

Concerned about another fiasco, after this week’s lab Danielle asked me how it worked out. I replied that, since the equipment we use for this week’s lab is all manually operated, there were no problems whatsoever. “Humans are much more capable than computers,” I said. “They’re able to cope with disruptions that computers have no hope of dealing with.”

The latest example of technology Hell appeared in a story in this morning’s (2/7/2019) Wall Street Journal. Some $136 million of customers’ cryptocurrency holdings became stuck in an electronic vault when the founder (and sole employee) of cryptocurrency exchange QuadrigaCX, Gerald Cotten, died of complications related to Crohn’s disease while building an orphanage in India. The problem is that Cotten was so secretive about passwords and security that nobody, even his wife, Jennifer Robertson, can get into the reserve account maintained on his laptop.

“Quadriga,” according to the WSJ account, “would need control of that account to send those funds to customers.”

No lie! The WSJ attests this bizarre tale is the God’s own truth!

Now, I’ve no sympathy for cryptocurrency mavens, which I consider to be, at best, technoweenies gleefully leading a parade down the primrose path to technology Hell, but this story illustrates what that Hell looks like!

It’s exactly what the Luddites of the early 19th Century warned us about. It’s a place of nameless frustration and unaccountable loss that we’ve brought on ourselves.

Why Diversity Rules

Diverse friends
A diverse group of people with different ages and nationalities having fun together. Rawpixel/Shutterstock

23 January 2019 – Last week two concepts reared their ugly heads that I’ve been banging on about for years. They’re closely intertwined, so it’s worthwhile to spend a little blog space discussing why they fit so tightly together.

Diversity is Good

The first idea is that diversity is good. It’s good in almost every human pursuit. I’m particularly sensitive to this, being someone who grew up with the idea that rugged individualism was the highest ideal.

Diversity, of course, is incompatible with individualism. Individualism is the cult of the one. “One” cannot logically be diverse. Diversity is a property of groups, and groups by definition consist of more than one.

Okay, set theory admits of groups with one or even no members, but those groups have a diversity “score” (Gini–Simpson index) of zero. To have any diversity at all, your group has to have at absolute minimum two members. The more the merrier (or diversitier).

The idea that diversity is good came up in a couple of contexts over the past week.

First, I’m reading a book entitled Farsighted: How We Make the Decisions That Matter the Most by Steven Johnson, which I plan eventually to review in this blog. Part of the advice Johnson offers is that groups make better decisions when their membership is diverse. How they are diverse is less important than the extent to which they are diverse. In other words, this is a case where quantity is more important than quality.

Second, I divided my physics-lab students into groups to perform their first experiment. We break students into groups to prepare them for working in teams after graduation. Unlike when I was a student fifty years ago, activity in scientific research and technology development is always done in teams.

When I was a student, research was (supposedly) done by individuals working largely in isolation. I believe it was Willard Gibbs (I have no reliable reference for this quote) who said: “An experimental physicist must be a professional scientist and an amateur everything else.”

By this he meant that building a successful physics experiment requires the experimenter to apply so many diverse skills that it is impossible to have professional mastery of all of them. He (or she) must have an amateur’s ability pick up novel skills in order to reach the next goal in their research. They must be ready to work outside their normal comfort zone.

That asked a lot from an experimental researcher! Individuals who could do that were few and far between.

Today, the fast pace of technological development has reduced that pool of qualified individuals essentially to zero. It certainly is too small to maintain the pace society expects of the engineering and scientific communities.

Tolkien’s “unimaginable hand and mind of Feanor” puttering around alone in his personal workshop crafting magical things is unimaginable today. Marlowe’s Dr. Faustus character, who had mastered all earthly knowledge, is now laughable. No one person is capable of making a major contribution to today’s technology on their own.

The solution is to perform the work of technological research and development in teams with diverse skill sets.

In the sciences, theoreticians with strong mathematical backgrounds partner with engineers capable of designing machines to test the theories, and technicians with the skills needed to fabricate the machines and make them work.

Chaotic Universe

The second idea I want to deal with in this essay is that we live in a chaotic Universe.

Chaos is a property of complex systems. These are systems consisting of many interacting moving parts that show predictable behavior on short time scales, but eventually foil the most diligent attempts at long-term prognostication.

A pendulum, by contrast, is a simple system consisting of, basically, three moving parts: a massive weight, or “pendulum bob,” that hangs by a rod or string (the arm) from a fixed support. Simple systems usually do not exhibit chaotic behavior.

The solar system, consisting of a huge, massive star (the Sun), eight major planets and a host of minor planets, is decidedly not a simple system. Its behavior is borderline chaotic. I say “borderline” because the solar system seems well behaved on short time scales (e.g., millennia), but when viewed on time scales of millions of years does all sorts of unpredictable things.

For example, approximately four and a half billion years ago (a few tens of millions of years after the system’s initial formation) a Mars-sized planet collided with Earth, spalling off a mass of material that coalesced to form the Moon, then ricochetted out of the solar system. That’s the sort of unpredictable event that happens in a chaotic system if you wait long enough.

The U.S. economy, consisting of millions of interacting individuals and companies, is wildly chaotic, which is why no investment strategy has ever been found to work reliably over a long time.

Putting It Together

The way these two ideas (diversity is good, and we live in a chaotic Universe) work together is that collaborating in diverse groups is the only way to successfully navigate life in a chaotic Universe.

An individual human being is so powerless that attempting anything but the smallest task is beyond his or her capacity. The only way to do anything of significance is to collaborate with others in a diverse team.

In the late 1980s my wife and I decided to build a house. To begin with, we had to decide where to build the house. That required weeks of collaboration (by our little team of two) to combine our experiences of different communities in the area where we were living, develop scenarios of what life might be like living in each community, and finally agree on which we might like the best. Then we had to find an architect to work with our growing team to design the building. Then we had to negotiate with banks for construction loans, bridge loans, and ultimate mortgage financing. Our architect recommended adding a prime contractor who had connections with carpenters, plumbers, electricians and so forth to actually complete the work. The better part of a year later, we had our dream house.

There’s no way I could have managed even that little project – building one house – entirely on my own!

In 2015, I ran across the opportunity to produce a short film for a film festival. I knew how to write a script, run a video camera, sew a costume, act a part, do the editing, and so forth. In short, I had all the skills needed to make that 30-minute film.

Did that mean I could make it all by my onesies? Nope! By the time the thing was completed, the list of cast and crew counted over a dozen people, each with their own job in the production.

By now, I think I’ve made my point. The take-home lesson of this essay is that if you want to accomplish anything in this chaotic Universe, start by assembling a diverse team, and the more diverse, the better!

Reimagining Our Tomorrows

Cover Image
Utopia with a twist.

19 December 2018 – I generally don’t buy into utopias.

Utopias are intended as descriptions of a paradise. They’re supposed to be a paradise for everybody, and they’re supposed to be filled with happy people committed to living in their city (utopias are invariably built around descriptions of cities), which they imagine to be the best of all possible cities located in the best of all possible worlds.

Unfortunately, however, utopia stories are written by individual authors, and they’d only be a paradise for that particular author. If the author is persuasive enough, the story will win over a following of disciples, who will praise it to high Heaven. Once in a great while (actually surprisingly often) those disciples become so enamored of the description that they’ll drop everything and actually attempt to build a city to match the description.

When that happens, it invariably ends in tears.

That’s because, while utopian stories invariably describe city plans that would be paradise to their authors, great swaths of the population would find living in them to be horrific.

Even Thomas More, the sixteenth century philosopher, politician and generally overall smart guy who’s credited with giving us the word “utopia” in the first place, was wise enough to acknowledge that the utopia he described in his most famous work, Utopia, wouldn’t be such a fun place for the slaves he had serving his upper-middle class citizens, who were the bulwark of his utopian society.

Even Plato’s Republic, which gave us the conundrum summarized in Juvenal’s Satires as “Who guards the guards?,” was never meant as a workable society. Plato’s work, in general, was meant to teach us how to think, not what to think.

What to think is a highly malleable commodity that varies from person to person, society to society, and, most importantly, from time to time. Plato’s Republic reflected what might have passed as good ideas for city planning in 380 BC Athens, but they wouldn’t have passed muster in More’s sixteenth-century England. Still less would they be appropriate in twenty-first-century democracies.

So, I approached Joe Tankersley’s Reimagining Our Tomorrows with some trepidation. I wouldn’t have put in the effort to read the thing if it wasn’t for the subtitle: “Making Sure Your Future Doesn’t SUCK.”

That subtitle indicated that Tankersley just might have a sense of humor, and enough gumption to put that sense of humor into his contribution to Futurism.

Futurism tends to be the work of self-important intellectuals out to make a buck by feeding their audience on fantasies that sound profound, but bear no relation to any actual or even possible future. Its greatest value is in stimulating profits for publishers of magazines and books about Futurism. Otherwise, they’re not worth the trees killed to make the paper they’re printed on.

Trees, after all and as a group, make a huge contribution to all facets of human life. Like, for instance, breathing. Breathing is of incalculable value to humans. Trees make an immense contribution to breathing by absorbing carbon dioxide and pumping out vast quantities of oxygen, which humans like to breathe.

We like trees!

Futurists, not so much.

Tankersley’s little (168 pages, not counting author bio, front matter and introduction) opus is not like typical Futurist literature, however. Well, it would be like that if it weren’t more like the Republic in that it’s avowed purpose is to stimulate its readers to think about the future themselves. In the introduction that I purposely left out of the page count he says:

I want to help you reimagine our tomorrows; to show you that we are living in a time when the possibility of creating a better future has never been greater.”

Tankersley structured the body of his book in ten chapters, each telling a separate story about an imagined future centered around a possible solution to an issue relevant today. Following each chapter is an “apology” by a fictional future character named Archibald T. Patterson III.

Archie is what a hundred years ago would have been called a “Captain of Industry.” Today, we’d refer to him as an uber-rich and successful entrepreneur. Think Elon Musk or Bill Gates.

Actually, I think he’s more like Warren Buffet in that he’s reasonably introspective and honest with himself. Archie sees where society has come from, how it got to the future it got to, and what he and his cohorts did wrong. While he’s super-rich and privileged, the futures the stories describe were made by other people who weren’t uber-rich and successful. His efforts largely came to naught.

The point Tankersley seems to be making is that progress comes from the efforts of ordinary individuals who, in true British fashion, “muddle through.” They see a challenge and apply their talents and resources to making a solution. The solution is invariably nothing anyone would foresee, and is nothing like what anyone else would come up with to meet the same challenge. Each is a unique response to a unique challenge by unique individuals.

It might seem naive, this idea that human development comes from ordinary individuals coming up with ordinary solutions to ordinary problems all banded together into something called “progress,” but it’s not.

For example, Mark Zuckerberg developed Facebook as a response to the challenge of applying then-new computer-network technology to the age-old quest by late adolescents to form their own little communities by communicating among themselves. It’s only fortuitous that he happened on the right combination of time (the dawn of a radical new technology), place (in the midst of a huge cadre of the right people well versed in using that radical new technology) and marketing to get the word out to those right people wanting to use that radical new technology for that purpose. Take away any of those elements and there’d be no Facebook!

What if Zuckerberg hadn’t invented Facebook? In that event, somebody else (Reid Hoffman) would have come up with a similar solution (Linkedin) to the same challenge facing a similar group (technology professionals).

Oh, my! They did!

History abounds with similar examples. There’s hardly any advancement in human culture that doesn’t fit this model.

The good news is that Tankersley’s vision for how we can re-imagine our tomorrows is right on the money.

The bad news is … there isn’t any bad news!

Computers Are Revolting!

Will Computers Revolt? cover
Charles Simon’s Will Computers Revolt? looks at the future of interactions between artificial intelligence and the human race.

14 November 2018 – I just couldn’t resist the double meaning allowed by the title for this blog posting. It’s all I could think of when reading the title of Charles Simon’s new book, Will Computers Revolt? Preparing for the Future of Artificial Intelligence.

On one hand, yes, computers are revolting. Yesterday my wife and I spent two hours trying to figure out how to activate our Netflix account on my new laptop. We ended up having to change the email address and password associated with the account. And, we aren’t done yet! The nice lady at Netflix sadly informed me that in thirty days, their automated system would insist that we re-log-into the account on both devices due to the change.

That’s revolting!

On the other hand, the uprising has already begun. Computers are revolting in the sense that they’re taking power to run our lives.

We used to buy stuff just by picking it off the shelf, then walking up to the check-out counter and handing over a few pieces of green paper. The only thing that held up the process was counting out the change.

Later, when credit cards first reared their ugly heads, we had to wait a few minutes for the salesperson to fill out a sales form, then run our credit cards through the machine. It was all manual. No computers involved. It took little time once you learned how to do it, and, more importantly, the process was pretty much the same everywhere and never changed, so once you learned it, you’d learned it “forever.”

Not no more! How much time do you, I, and everyone else waste navigating the multiple pages we have to walk through just to pay for anything with a credit or debit card today?

Even worse, every store has different software using different screens to ask different questions. So, we can’t develop a habitual routine for the process. It’s different every time!

Not long ago the banks issuing my debit-card accounts switched to those %^&^ things with the chips. I always forget to put the thing in the slot instead of swiping the card across the magnetic-stripe reader. When that happens we have to start the process all over, wasting even more time.

The computers have taken over, so now we have to do what they tell us to do.

Now we know who’ll be first against the wall when the revolution comes. It’s already here and the first against the wall is us!

Golem Literature in Perspective

But seriously folks, Simon’s book is the latest in a long tradition of works by thinkers fascinated by the idea that someone could create an artifice that would pass for a human. Perhaps the earliest, and certainly the most iconic, is the golem stories from Jewish folklore. I suspect (on no authority, whatsoever, but it does seem likely) that the idea of a golem appeared about the time when human sculptors started making statues in realistic human form. That was very early, indeed!

A golem is, for those who aren’t familiar with the term or willing to follow the link provided above to learn about it, an artificial creature fashioned by a human that is effectively what we call a “robot.” The folkloric golems were made of clay or (sometimes) wood because those were the best materials available at the time that the stories’ authors’ could have their artists work with. A well-known golem story is Carlo Collodi’s The Adventures of Pinocchio.

By the sixth century BCE, Greek sculptors had begun to produce lifelike statues. The myth of Pygmalion and Galatea appeared in a pseudo-historical work by Philostephanus Cyrenaeus in the third century BCE. Pygmalion was a sculptor who made a statue representing his ideal woman, then fell in love with it. Aphrodite granted his prayer for a wife exactly like the statue by bringing the statue to life. The wife’s name was Galatea.

The Talmud points out that Adam started out as a golem. Like Galatea, Adam was brought to life when the Hebrew God Yahweh gave him a soul.

These golem examples emphasize the idea that humans, no matter how holy or wise, cannot give their creations a soul. The best they can do is to create automatons.

Simon effectively begs to differ. He spends the first quarter of his text laying out the case that it is possible, and indeed inevitable, that automated control systems displaying artificial general intelligence (AGI) capable of thinking at or (eventually) well above human capacity will appear. He spends the next half of his text showing how such AGI systems could be created and making the case that they will eventually exhibit functionality indistinguishable from consciousness. He devotes the rest of his text to speculating about how we, as human beings, will likely interact with such hyperintelligent machines.

Spoiler Alert

Simon’s answer to the question posed by his title is a sort-of “yes.” He feels AGIs will inevitably displace humans as the most intelligent beings on our planet, but won’t exactly “revolt” at any point.

“The conclusion,” he says, “is that the paths of AGIs and humanity will diverge to such an extent that there will be no close relationship between humans and our silicon counterparts.”

There won’t be any violent conflict because robotic needs are sufficiently dissimilar to ours that there won’t be any competition for scarce resources, which is what leads to conflict between groups (including between species).

Robots, he posits, are unlikely to care enough about us to revolt. There will be no Terminator robots seeking to exterminate us because they won’t see us as enough of a threat to bother with. They’re more likely to view us much the way we view squirrels and birds: pleasant fixtures of the natural world.

They won’t, of course, tolerate any individual humans who make trouble for them the same way we wouldn’t tolerate a rabid coyote. But, otherwise, so what?

So, the !!!! What?

The main value of Simon’s book is not in its ultimate conclusion. That’s basically informed opinion. Rather, its value lies in the voluminous detail he provides in getting to that conclusion.

He spends the first quarter of his text detailing exactly what he means by AGI. What functions are needed to make it manifest? How will we know when it rears its head (ugly or not, as a matter of taste)? How will a conscious, self-aware AGI system act?

A critical point Simon makes in this section is the assertion that AGI will arise first in autonomous mobile robots. I thoroughly agree for pretty much the same reasons he puts forth.

I first started seriously speculating about machine intelligence back in the middle of the twentieth century. I never got too far – certainly not as far as Simon gets in this volume – but pretty much the first thing I actually did realize was that it was impossible to develop any kind of machine with any recognizable intelligence unless its main feature was having a mobile body.

Developing any AGI feature requires the machine to have a mobile body. It has to take responsibility not only for deciding how to move itself about in space, but figuring out why. Why would it, for example, rather be over there, rather than to just stay here? Note that biological intelligence arose in animals, not in plants!

Simultaneously with reading Simon’s book, I was re-reading Robert A. Heinlein’s 1966 novel The Moon is a Harsh Mistress, which is one of innumerable fiction works whose plot hangs on actions of a superintelligent sentient computer. I found it interesting to compare Heinlein’s early fictional account with Simon’s much more informed discussion.

Heinlein sidesteps the mobile-body requirement by making his AGI arise in a computer tasked with operating the entire infrastructure of the first permanent human colony on the Moon (more accurately in the Moon, since Heinlein’s troglodytes burrowed through caves and tunnels, coming up to the surface only reluctantly when circumstances forced them to). He also avoids trying to imagine the AGI’s inner workings, by glossing over with the 1950s technology he was most familiar with.

In his rather longish second section, Simon leads his reader through a thought experiment speculating about what components an AGI system would need to have for its intelligence to develop. What sorts of circuitry might be needed, and how might it be realized? This section might be fascinating for those wanting to develop hardware and software to support AGI. For those of us watching from our armchairs on the outside, though, not so much.

Altogether, Charles Simon’s Will Computers Revolt? is an important book that’s fairly easy to read (or, at least as easy as any book this technical can be) and accessible to a wide range of people interested in the future of robotics and artificial intelligence. It is not the last word on this fast-developing field by any means. It is, however, a starting point for the necessary debate over how we should view the subject. Do we have anything to fear? Do we need to think about any regulations? Is there anything to regulate and would any such regulations be effective?

You Want to Print WHAT?!

3D printed plastic handgun
The Liberator gun, designed by Defense Distributed. Photo originally made at 16-05-2013 by Vvzvlad – Flickr: Liberator.3d.gun.vv.01, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=26141469

22 August 2018 – Since the Fifteenth Century, when Johannes Gutenberg invented the technology, printing has given those in authority ginky fits. Until recently it was just dissemination of heretical ideas, a la Giordano Bruno, that raised authoritarian hackles. More recently, 3-D printing, also known as additive manufacturing (AM), has made it possible to make things that folks who like to tell you what you’re allowed to do don’t want you to make.

Don’t get me wrong, AM makes it possible to do a whole lot of good stuff that we only wished we could do before. Like, for example, Jay Leno uses it to replace irreplaceable antique car parts. It’s a really great technology that allows you to make just about anything you can describe in a digital drafting file without the difficulty and mess of hiring a highly skilled fabrication machine shop.

In my years as an experimental physicist, I dealt with fabrication shops a lot! Fabrication shops are collections of craftsmen and the equipment they need to make one-off examples of amazing stuff. It’s generally stuff, however, that nobody’d want to make a second time.

Like the first one of anything.

The reason I specify that AM is for making stuff nobody’d want to make a second time is that it’s slow. Regular machine-shop work, like making nuts and bolts, and sewing machines, and stuff folks want to make a lot of are worth spending a lot of time to figure out fast, efficient and cheap ways to make lots of them.

Take microchips. The darn things take huge amounts of effort to design, and tens of billions of dollars of equipment to make, but once you’ve figured it all out and set it all up, you can pop the things out like chocolate-chip cookies. You can buy an Intel Celeron G3900 dual-core 2.8 GHz desktop processor online for $36.99 because Intel spread the hideous quantities of up-front cost it took to originally set up the production line over the bazillions of processors that production line can make. It’s called “economy of scale.”

If you’re only gonna make one, or just a few, of the things, there’s no economy of scale.

But, if your’re only gonna make one, or just a few, you don’t worry too much about how long it takes to make each one, and what it costs is what it costs.

So, you put up with doing it some way that’s slow.

Like AM.

A HUGE advantage of making things with AM is that you don’t have to be all that smart. If you once learn to set the 3-D printer up, you’re all set. You just download the digital computer-aided-manufacturing (CAM) file into the printer’s artificial cerebrum, and it just DOES it. If you can download the file over the Internet, you’ve got it knocked!

Which brings us to what I want to talk about today: 3-D printing of handguns.

Now, I’m not any kind of anti-gun nut. I’ve got half a dozen firearms laying around the house, and have had since I was a little kid. It’s a family thing. I learned sharpshooting when I was around ten years old. Target shooting is a form of meditation for me. A revolver is my preferred weapon if ever I have need of a weapon. I never want to be the one who brought a knife to a gunfight!

That said, I’ve never actually found a need for a handgun. The one time I was present at a gunfight, I hid under the bed. The few times I’ve been threatened with guns, I talked my way out of it. Experience has led me to believe that carrying a gun is the best way to get shot.

I’ve always agreed with my gunsmith cousin that modern guns are beautiful pieces of art. I love their precision and craftsmanship. I appreciate the skill and effort it takes to make them.

The good ones, that is.

That’s the problem with AM-made guns. It takes practically no skill to create them. They’re right up there with the zip guns we talked about when we were kids.

We never made zip guns. We talked about them. We talked about them in the same tones we’d use talking about leeches or cockroaches. Ick!

We’d never make them because they were beneath contempt. They were so crude we wouldn’t dare fire one. Junk like that’s dangerous!

Okay, so you get an idea how I would react to the news that some nut case had published plans online for 3-D printing a handgun. Bad enough to design such a monstrosity, what about the idiot stupid enough to download the plans and make such a thing? Even more unbelievable, what moron would want to fire it?

Have they no regard for their hands? Don’t they like their fingers?

Anyway, not long ago, a press release crossed my desk from Giffords, the gun-safety organization set up by former U.S. Rep. Gabrielle Giffords and her husband after she survived an assassination attempt in Arizona in 2011. The Giffords’ press release praised Senators Richard Blumenthal and Bill Nelson, and Representatives David Cicilline and Seth Moulton for introducing legislation to stop untraceable firearms from further proliferation after the Trump Administration cleared the way for anyone to 3-D-print their own guns.

Why “untraceable” firearms, and what have they got to do with AM?

Downloadable plans for producing guns by the AM technique puts firearms in the hands of folks too unskilled and, yes, stupid to make them themselves with ordinary methods. That’s important because it is theoretically possible to AM produce firearms of surprising sophistication. The first one offered was a cheap plastic thing (depicted above) that would likely be more danger to its user than its intended victim. More recent offerings, however, have been repeating weapons made of more robust materials that might successfully be used to commit crimes.

Ordinary firearm production techniques require a level of skill and investment in equipment that puts them above the radar for federal regulators. The first thing those regulators require is licensing the manufacturer. Units must be serialized, and records must be kept. The system certainly isn’t perfect, but it gives law enforcement a fighting chance when the products are misused.

The old zip guns snuck in under the radar, but they were so crude and dangerous to their users that they were seldom even made. Almost anybody with enough sense to make them had enough sense not to make them! Those who got their hands on the things were more a danger to themselves than to society.

The Trump administration’s recent settlement with Defense Distributed, allowing them to relaunch their website, include a searchable database of firearm blueprints, and allow the public to create their own fully-functional, unserialized firearms using AM technology opens the floodgates for dangerous people to make their own untraceable firearms.

That’s just dumb!

The Untraceable Firearms Act would prohibit the manufacture and sale of firearms without serial numbers, require any person or business engaged in the business of selling firearm kits and unfinished receivers to obtain a dealer’s license and conduct background checks on purchasers, and mandate that a person who runs a business putting together firearms or finishing receivers must obtain a manufacturer’s license and put serial numbers on firearms before offering them for sale to consumers.

The 3-D Printing Safety Act would prohibit the online publication of computer-aided design (CAD) files which automatically program a 3-D-printer to produce or complete a firearm. That closes the loophole letting malevolent idiots evade scrutiny by making their own firearms.

We have to join with Giffords in applauding the legislators who introduced these bills.

Do You Really Want a Robotic Car?

Robot Driver
Sixteen percent of Canadians and twenty-six percent of Americans say they “would not use a driverless car.” Mopic/Shutterstock

15 August 2018 – Many times in my blogging career I’ve gone on a rant about the three Ds of robotics. These are “dull, dirty, and dangerous.” They relate to the question, which is not asked often enough, “Do you want to build an automated system to do that task?”

The reason the question is not asked enough is that it needs to be asked whenever anyone looks into designing a system (whether manual or automated) to do anything. The possibility that anyone ever sets up a system to do anything without first asking that question means that it’s not asked enough.

When asking the question, getting a hit on any one of the three Ds tells you to at least think about automating the task. Getting a hit on two of them should make you think that your task is very likely to be ripe for automation. If you hit on all three, it’s a slam dunk!

When we look into developing automated vehicles (AVs), we get hits on “dull” and “dangerous.”

Driving can be excruciatingly dull, especially if you’re going any significant distance. That’s why people fall asleep at the wheel. I daresay everyone has fallen asleep at the wheel at least once, although we almost always wake up before hitting the bridge abutment. It’s also why so many people drive around with cellphones pressed to their ears. The temptation to multitask while driving is almost irresistable.

Driving is also brutally dangerous. Tens of thousands of people die every year in car crashes. Its pretty safe to say that nearly all those fatalities involve cars driven by humans. The number of people who have been killed in accidents involving driverless cars you can (as of this writing) count on one hand.

I’m not prepared to go into statistics comparing safety of automated vehicles vs. manually driven ones. Suffice it to say that eventually we can expect AVs to be much safer than manually driven vehicles. We’ll keep developing the technology until they are. It’s not a matter of if, but when.

This is the analysis most observers (if they analyze it at all) come up with to prove that vehicle driving should be automated.

Yet, opinions that AVs are acceptable, let alone inevitable, are far from universal. In a survey of 3,000 people in the U.S. and Canada, Ipsos Strategy 3 found that sixteen percent of Canadians and twenty six percent of Americans say they “would not use a driverless car,” and a whopping 39% of Americans (and 30% of Canadians) would rarely or never let driverless technology do the parking!

Why would so many people be unwilling to give up their driving priviledges? I submit that it has to do with a parallel consideration that is every bit as important as the three Ds when deciding whether to automate a task:

Don’t Automate Something Humans Like to Do!

Predatory animals, especially humans, like to drive. It’s fun. Just ask any dog who has a chance to go for a ride in a car! Almost universally they’ll jump into the front seat as soon as you open the door.

In fact, if you leave them (dogs) in the car unattended for a few minutes, they’ll be behind the wheel when you come back!

Humans are the same way. Leave ’em unattended in a car for any length of time, and they’ll think of some excuse to get behind the wheel.

The Ipsos survey fourd that some 61% of both Americans and Canadians identify themselves as “car people.” When asked “What would have to change about transportation in your area for you to consider not owning a car at all, or owning fewer cars?” 39% of Americans and 38% of Canadians responded “There is nothing that would make me consider owning fewer cars!”

That’s pretty definative!

Their excuse for getting behind the wheel is largely an economic one: Seventy-eight pecent of Americans claim they “definitely need to have a vehicle to get to work.” In more urbanized Canada (you did know that Canadians cluster more into cities, didn’t you.) that drops to 53%.

Whether those folks claiming they “have” to have a car to get to work is based on historical precedent, urban planning, wishful thinking, or flat out just what they want to believe, it’s a good, cogent reason why folks, especially Americans, hang onto their steering wheels for dear life.

The moral of this story is that driving is something humans like to do, and getting them to give it up will be a serious uphill battle for anyone wanting to promote driverless cars.

Yet, development of AV technology is going full steam ahead.

Is that just another example of Dr. John Bridges’  version of Solomon’s proverb “A fool and his money are soon parted?”

Possibly, but I think not.

Certainly, the idea of spending tons of money to have bragging rights for the latest technology, and to take selfies showing you reading a newspaper while your car drives itself through traffic has some appeal. I submit, however, that the appeal is short lived.

For one thing, reading in a moving vehicle is the fastest route I know of to motion sickness. It’s right up there with cueing up the latest Disney cartoon feature for your kids on the overhead DVD player in your SUV, then cleaning up their vomit.

I, for one, don’t want to go there!

Sounds like another example of “More money than brains.”

There are, however, a wide range of applications where driving a vehicle turns out to be no fun at all. For example, the first use of drone aircraft was as targets for anti-aircraft gunnery practice. They just couldn’t find enough pilots who wanted to be sitting ducks to be blown out of the sky! Go figure.

Most commercial driving jobs could also stand to be automated. For example, almost nobody actually steers ships at sea anymore. They generally stand around watching an autopilot follow a pre-programmed course. Why? As a veteran boat pilot, I can tell you that the captain has a lot more fun than the helmsman. Piloting a ship from, say, Calcutta to San Francisco has got to be mind-numbingly dull. There’s nothing going on out there on the ocean.

Boat passengers generally spend most of their time staring into the wake, but the helmsman doesn’t get to look at the wake. He (or she) has to spend their time scanning a featureless horizontal line separating a light-blue dome (the sky) from a dark-blue plane (the sea) in the vain hope that something interesting will pop up and relieve the tedium.

Hence the autopilot.

Flying a commercial airliner is similar. It has been described (as have so many other tasks) as “hours of boredom punctuated by moments of sheer terror!” While such activity is very Zen (I’m convinced that humans’ ability to meditate was developed by cave guys having to sit for hours, or even days, watching game trails for their next meal to wander along), it’s not top-of-the-line fun.

So, sometimes driving is fun, and sometimes it’s not. We need AV technology to cover those times when it’s not.